Sensitivity and specificity were calculated by comparing the automated classifier results with the true margin status, determined from co-registered histology. 83.3% sensitivity and 86.2% specificity were achieved, compared to 69.0% sensitivity and 79.0% specificity obtained with OCT alone on the same dataset using human readers. Representative optical palpograms show that positive margins containing a range of cancer types tend to exhibit higher stress compared to negative margins. These results demonstrate the potential of optical palpation for margin assessment.We have developed a flexible optical imaging system (FOIS) to assess systemic lupus erythematosus (SLE) arthritis in the finger joints. While any part of the body can be affected, arthritis in the finger joints is one of the most common SLE manifestations. There is an unmet need for accurate, low-cost assessment of lupus arthritis that can be easily performed at every clinic visit. Current imaging methods are imprecise, expensive, and time consuming to allow for frequent monitoring. Our FOIS can be wrapped around joints, and multiple light sources and detectors gather reflected and transmitted light intensities. Using data from two SLE patients and two healthy volunteers, we demonstrate the potential of this FOIS for assessment of arthritis in SLE patients.Multimodal data fusion is one of the current primary neuroimaging research directions to overcome the fundamental limitations of individual modalities by exploiting complementary information from different modalities. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are especially compelling modalities due to their potentially complementary features reflecting the electro-hemodynamic characteristics of neural responses. However, the current multimodal studies lack a comprehensive systematic approach to properly merge the complementary features from their multimodal data. Identifying a systematic approach to properly fuse EEG-fNIRS data and exploit their complementary potential is crucial in improving performance. This paper proposes a framework for classifying fused EEG-fNIRS data at the feature level, relying on a mutual information-based feature selection approach with respect to the complementarity between features. The goal is to optimize the complementarity, redundancy and relevance between multimodal features with respect to the class labels as belonging to a pathological condition or healthy control. Nine amyotrophic lateral sclerosis (ALS) patients and nine controls underwent multimodal data recording during a visuo-mental task. Multiple spectral and temporal features were extracted and fed to a feature selection algorithm followed by a classifier, which selected the optimized subset of features through a cross-validation process. The results demonstrated considerably improved hybrid classification performance compared to the individual modalities and compared to conventional classification without feature selection, suggesting a potential efficacy of our proposed framework for wider neuro-clinical applications.Brain tumor surgery involves a delicate balance between maximizing the extent of tumor resection while minimizing damage to healthy brain tissue that is vital for neurological function. However, differentiating between tumor, particularly infiltrative disease, and healthy brain in-vivo remains a significant clinical challenge. Here we demonstrate that quantitative oblique back illumination microscopy (qOBM)-a novel label-free optical imaging technique that achieves tomographic quantitative phase imaging in thick scattering samples-clearly differentiates between healthy brain tissue and tumor, including infiltrative disease. Data from a bulk and infiltrative brain tumor animal model show that qOBM enables quantitative phase imaging of thick fresh brain tissues with remarkable cellular and subcellular detail that closely resembles histopathology using hematoxylin and eosin (H&E) stained fixed tissue sections, the gold standard for cancer detection. Quantitative biophysical features are also extracted from qOBM which yield robust surrogate biomarkers of disease that enable (1) automated tumor and margin detection with high sensitivity and specificity and (2) facile visualization of tumor regions. Finally, we develop a low-cost, flexible, fiber-based handheld qOBM device which brings this technology one step closer to in-vivo clinical use. This work has significant implications for guiding neurosurgery by paving the way for a tool that delivers real-time, label-free, in-vivo brain tumor margin detection.In this study, an optical contactless laser speckle imaging technique for the early identification of bacterial colony-forming units was tested. The aim of this work is to compare the laser speckle imaging method for the early assessment of microbial activity with standard visual inspection under white light illumination. In presented research, the growth of Vibrio natriegens bacterial colonies on the solid medium was observed and analyzed. Both - visual examination under white light illumination and laser speckle correlation analysis were performed.  https://www.selleckchem.com/products/Cladribine.html  Based on various experiments and comparisons with the theoretical Gompertz model, colony radius growth curves were obtained. It was shown that the Gompertz model can be used to describe both types of analysis. A comparison of the two methods shows that laser speckle contrast imaging, combined with signal processing, can detect colony growth earlier than standard CFU counting method under white light illumination.Digital pathology has shown great importance for diagnostic purposes in the digital age by integrating basic image features into multi-modality information. We quantify the degree of correlation between the multiple texture features from H&E images and polarization parameter sets derived from Mueller matrix images of the same sample to provide more microstructural information for assisting diagnosis. The experimental result shows the correlations between texture feature and polarization parameter via Pearson coefficients. Polarization parameters t1 , DL and the depolarization parameter Δ correlated with image texture features Tamura_Fcon and Tamura_Frgh, and can be used as powerful tools to quantitatively characterize cell nuclei related with tumor progression in breast pathological tissues. Polarization parameters δ and rL associated with the image texture feature Tamura_Flin have great potential for the quantitative characterization of proliferative fibers produced by inflammation. Furthermore, polarization parameters have the advantages of stable recognition in low resolution images.